h0rton.tdlmc_utils package

h0rton.tdlmc_utils.reorder_images module

h0rton.tdlmc_utils.reorder_images.reorder_to_tdlmc(abcd_ordering_i, ra_img, dec_img, time_delays)

Reorder the list of ra, dec, and time delays to conform to the order in the TDLMC challenge

abcd_ordering_i : array-like

ABCD in an increasing dec order if the keys ABCD mapped to values 0123, respectively, e.g. [3, 1, 0, 2] if D (value 3) is lowest, B (value 1) is second lowest

ra_img : array-like

list of ra from lenstronomy

dec_img : array-like

list of dec from lenstronomy, in the order specified by ra_img

time_delays : array-like

list of time delays from lenstronomy, in the order specified by ra_img

tuple

tuple of (reordered ra, reordered_dec, reordered time delays)

h0rton.tdlmc_utils.tdlmc_parser module

h0rton.tdlmc_utils.tdlmc_parser.convert_to_dataframe(rung, save_csv_path)

Store the TDLMC closed and open boxes into a Pandas DataFrame and exports to a csv file at the same location

rung : int

rung number

save_csv_path : str

path of the csv file to be generated

Pandas DataFrame

the extracted rung data

h0rton.tdlmc_utils.tdlmc_parser.parse_closed_box(closed_box_path, row_dict={})

Parse the lines of an open-box TDLMX text file for Rungs 0, 1, and 2

closed_box_path : str

path to the closed box text file, lens_info_for_Good_team.txt.txt

row_dict : dict

dictionary of the row info to update. Default: dict()

dict

An updated dictionary containing the information in the closed box text file

h0rton.tdlmc_utils.tdlmc_parser.parse_open_box(open_box_path, row_dict={})

Parse the lines of an open-box TDLMX text file for Rungs 0, 1, and 2

open_box_path : str

path to the open box text file, lens_all_info.txt

row_dict : dict

dictionary of the row info to update. Default: dict()

dict

An updated dictionary containing the information in the open box text file

h0rton.tdlmc_utils.tdlmc_parser.read_from_csv(csv_path)

Read a Pandas Dataframe from the combined csv file of TDLMC data while evaluating all the relevant strings in each column as Python objects

csv_path : str

path to the csv file generated using convert_to_dataframe

Pandas DataFrame

the TDLMC data with correct Python objects

h0rton.tdlmc_utils.tdlmc_parser.format_results_for_tdlmc_metrics(version_dir, out_dir, rung_id=2)

Format the BNN inference results so they can be read into the script that generates the TDLMC metrics cornerplot

version_dir : str or os.path object

path to the folder containing inference results

rung_id : int

TDLMC rung ID